A Systemic Approach to Poverty Alleviation: Expanding Opportunities and Capability Enhancement

This paper presents a scalable framework for poverty alleviation that leverages behavioral decision metrics to identify actionable pathways for improving income stability and social mobility. Drawing theoretical inspiration from behavioral genetics, the framework relies solely on observable behaviors and environmental factors—no genetic testing is required. Key behavioral dimensions, including time preference, risk tolerance, and self-control, are measured through brief questionnaires and interviews. Preliminary simulations suggest potential poverty reductions of 20–25% in urban populations, highlighting its practical impact and cost-effectiveness. The methodology is suitable for implementation by NGOs, NPOs, and policy makers, providing a scientifically grounded, ethical, and low-cost approach to poverty reduction. Key Points Ethical and Practical: No DNA or genetic data collection; relies on standard behavioral metrics. Scalable: 30-minute assessment enables rapid, low-cost implementation. Behavior-Focused: Targets decision-making traits and environmental factors influencing economic outcomes. High Impact: Simulations suggest 20–25% poverty rate reduction in urban populations. Policy and NGO Ready: Suitable for integration into social programs without large budgets. “The theoretical framework enables the design and implementation of targeted poverty reduction strategies through AI. By incorporating demographic, economic, and behavioral data, AI can generate individualized intervention plans, prioritize actionable steps, and simulate potential outcomes, facilitating rapid, data-driven decision-making without relying on genetic information.”